Solar Photovoltaic Generation Research Articles

Fast Frequency Response for Future Low-Carbon Indian Power System: Challenges and Solutions

India has transitioned from being an energy-deficient nation to an energy-surplus nation. As of July 2023, India’s power system has seen a rapid transformation, with renewable energy, including large hydro, accounting for 40% of the total installed capacity at 177 GW. The ongoing energy transition has not only changed the country’s energy mix but also brought new challenges for system operators. The primary challenge is frequency instability due to the reduction of system inertia and governor-based primary frequency response (PFR) availability by large-scale integration of uncertain renewable energy resources (RES) such as wind and solar photovoltaic generators. Furthermore, the absence of frequency control ancillary services market, inadequate penetration of electric vehicles (EVs) and uncertainty of RES generation enhance the complexity of the frequency response services in the Indian power system. In this context, this paper presents a comprehensive review of the frequency response challenges of the future low-carbon Indian power system and underlines the new potential fast frequency response (FFR) services from advanced energy storage and renewable generation technologies. Such FFR services can be swiftly deployed in low-inertia grids through the integration of energy storage systems and inverter-based resources in a shorter span. Modeling of FFR services in security constraint unit commitment (SCUC) can enhance grid resiliency and facilitate high penetration of RES in the Indian power system without loss of frequency stability.

Design and Implementation of Single-Phase Grid-Connected Low-Voltage Battery Inverter for Residential Applications

Integrating residential energy storage and solar photovoltaic power generation into low-voltage distribution networks is a pathway to energy self-sufficiency. This paper elaborates on designing and implementing a 3 kW single-phase grid-connected battery inverter to integrate a 51.2-V lithium iron phosphate battery pack with a 220 V 50 Hz grid. The prototyped inverter consists of an LCL-filtered voltage source converter (VSC) and a dual active bridge (DAB) DC-DC converter, both operated at a switching frequency of 20 kHz. The VSC adopted a fast DC bus voltage control strategy with a unified current harmonic mitigation. Meanwhile, the DAB DC-DC converter employed a proportional-integral regulator to control the average battery current with a dynamic DC offset mitigation of the medium-frequency transformer’s currents embedded in the single-phase shift modulation scheme. The control schemes of the two converters were implemented on a 32-bit TMS320F280049C microcontroller in the same interrupt service routine. This work presents a synchronization technique between the switching signal generation of the two converters and the sampling of analog signals for the control system. The prototyped inverter had an efficiency better than 90% and a total harmonic distortion in the grid current smaller than 1.5% at the battery power of ±1.5 kW.

Morphology Optimization of Residential Communities towards Maximizing Energy Self-Sufficiency in the Hot Summer Cold Winter Climate Zone of China

Further research is needed on the capability of residential communities to achieve energy self-sufficiency under the constraints of current standards of land use, in particular for the Hot Summer and Cold Winter climate zone (HSCW) of China, where the majority of communities are dominated by high floor-area ratios, thus high-rise dwellings, namely less solar potential per unit floor area, while most residents adopt a “part-time, part-space” pattern of intermittent energy use behavior, thus using relatively low energy per unit floor area. This study examines 150 communities in Changsha to identify morphological indicators and develop a prototype model utilizing the Grasshopper platform. Community morphology is simulated and optimized by taking building location, orientation, and number of floors as independent variables and building energy consumption, solar PV generation, and energy self-sufficiency rate as dependent variables. The results reveal that the morphology optimization can achieve a 4.26% decrease in building energy consumption, a 45% increase in PV generation, and a 13.2% enhancement in energy self-sufficiency, with the optimal being 39%. It highlights that energy self-sufficiency cannot be achieved solely through morphology improvements. Moreover, the study underscores the crucial role of community orientation in maximizing energy self-sufficiency, with the south–north orientation identified as the most beneficial. Additionally, a layout characterized by a horizontally closed and staggered pattern and a vertically scattered arrangement emerges as favorable for enhancing energy self-sufficiency. These findings underscore the importance of considering morphological factors, particularly community orientation, in striving towards energy-self-sufficient high-rise residential communities within the HSCW climate zone of China.

Spatio-Temporal Deep Learning-Based Forecasting of Surface Solar Irradiance: Leveraging Satellite Data and Feature Selection

Spatio-Temporal Deep Learning-Based Forecasting of Surface Solar Irradiance: Leveraging Satellite Data and Feature Selection

Research on optimal dispatching strategy of solar thermal-photovoltaic-wind combined power generation system

New energy photovoltaic generation of electricity and wind power have developed rapidly, and the installed capacity has been increasing, but their volatility and randomness have also caused a certain impact on the power grid. Considering that solar thermal power generation and photovoltaic power generation have natural complementary advantages, and solar thermal power generation and power output of wind power can complement each other in time., this paper proposes an optimal scheduling strategy for solar thermal-photovoltaic-wind alliance power generation system, which uses thermal storage device of the heat power station of solar energy to cope with the problem of wind and light abandonment of new energy power generation and promote the consumption level of renewable energy. The economic and stability deviations of the system before and in the launch the solar thermal power station are compared in depth and the simulation is verified in the IEEE30 node system. The results show that after the introduction of solar thermal power (CSP) station in the combined power generation system, the economy of the scientific is remarkable improvement, the wind turbine is also reduced, which realizes the full absorption of new energy.

Assessment of photovoltaic generation penetration effect on the maximum loadability of the system

This paper proposed a method to investigate the effect of increasing PV penetration on the voltage stability of an IEEE 14-bus test system considering maximum PV penetration and system loadability limit. The critical bus of the test system has been decided based on nose curve analysis. The solar PV system is deployed with the most critical bus of the system. The effect of increasing PV penetration on the improvement in the loading capacity of various power system components like transmission lines, transmission line transformers, and generators is investigated over the adopted test system. Based on solar PV penetration up to 100 MW, the maximum loadability limit of the IEEE 14-bus test system increases, as shown by the results. Bus 14 is found to be the most severe bus of the test system using the continuation power flow (CPF) method. However, in some cases, overloading situations develop after a certain limit of PV penetration in the power system. In this condition, the overloading of the power system equipment is improved by the use of a Static Synchronous Compensator (STATCOM) at bus number 14, a double circuit line in the critical line (9–14), and Static Synchronous Series Compensator (SSSC) in the most severe line (9–14) in the system. The maximum loadability of the system gets maximum enhanced from 4.0349 p.u. to 4.5602 p.u. under simultaneous use of solar PV generation, SSSC, and STATCOM at the most critical bus and in most severe line of the system. As evidence, the enhancement in maximum loadability of the system found using the proposed method has been also compared with the existing research. The maximum system loadability has been also enhanced under normal and (N-1) contingency conditions by the use of PV penetration in the system. PSAT/MATLAB software is used for simulation and maximum loadability has been investigated by continuation power flow (CPF) method.

The Application Status and Prospects of Solar Photovoltaic Power Generation Technology in China

Solar photovoltaic power generation, as an environmentally friendly energy technology that converts sunlight into electricity, directly converts sunlight into electricity through the use of solar panels, further producing clean and environmentally friendly electricity. Through the analysis of the development status of China's solar photovoltaic power generation, this article discusses the development direction of China's solar photovoltaic power generation to provide reference for the healthy development of China's solar photovoltaic power generation industry.

Green energy-sourced AI-controlled multilevel UPQC parameter selection using football game optimization

The power quality (PQ) has been significantly affected by the integration of intermittent non-conventional sources (NCS) into the local distribution system in addition to the adoption of power electronic technologies to regulate non-linear loads. This article combines the H-bridge cascade five-level unified power quality conditioner (5L-UPQC) with the wind power generation system (WPGS), solar photovoltaic power generation system (SPVGS), and battery storage system (BSS) as an effective approach to address PQ problems. The utilization of the Levenberg–Marquardt backpropagation (LMBP)-trained Artificial neural network controller (ANNC) in the UPQC is recommended for generating appropriate reference signals for the converters. This eliminates the requirement for conventional complex conversions, such as abc, dq0, and αβ. Moreover, the artificial neuro-fuzzy interface system (ANFIS) is recommended for achieving a DC-link balance. Football game optimization (FBGO) is utilized to determine the optimal shunt and series filter characteristics. The major objectives of the proposed system are to reduce the current waveform irregularities, resulting in a decrease in the total harmonic distortion (THD), an enhancement in the power factor (PF), the mitigation of supply voltage imbalances and disturbances, and the maintenance of a steady direct-current link capacitor voltage (DLCV), despite the variations in the load, solar irradiation, and wind velocity. The efficiency of the suggested strategy is assessed using four case studies that involve different loads, variable wind velocities, and source voltage balancing conditions. Based on the simulation studies and obtained results, the suggested method significantly decreases the THD to values of 2.91%, 3.63%, 3.75%, and 3.50%. Additionally, it achieves a power factor of unity, which is considerably lower compared to other multilevel schemes that use the traditional symmetrical reference frame (SRF) and instantaneous reactive power (pq) methods. This design has been executed using the MATLAB/Simulink program.

Scheme Design and Energy-Saving Optimization of Cold and Heat Energy Supply System for Substation Main Control Building in Cold Area

In the context of global climate change, the implementation of building energy conservation and carbon reduction, as well as the realization of zero-energy buildings, is a key measure to cope with climate change and resource depletion. A substation is an indispensable building in the process of urbanization construction. However, in existing cold areas, the heating form of substations generally adopts electric heating, which consumes a large amount of energy. This paper optimizes the existing HVAC form of substations through the rational utilization of surrounding environmental resources and puts forward reasonable building energy-saving and carbon-reduction methods. It demonstrates the feasibility of combining solar photovoltaic power generation systems, air source heat pumps, and natural ventilation to optimize energy savings and carbon reduction in the main control building of a substation in a cold area. The computational fluid dynamics (CFD) method is used to demonstrate the feasibility of natural ventilation during the summer and transition seasons. The data indicate that the installation of a solar photovoltaic power generation system results in an average annual power generation of 18.75 MWh. Additionally, using an air source heat pump can save 44.5% of electricity compared to electric heating. When both a solar photovoltaic power generation system and an air source heat pump are used to provide a building with cold and heat sources, the annual emissions of CO2 can be reduced by 4.90 tons compared to a traditional electric heating system.

A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources

This paper presents a novel approach to solve the optimal power flow (OPF) problem by utilizing a modified white shark optimization (MWSO) algorithm. The MWSO algorithm incorporates the Gaussian barebones (GB) and quasi-oppositional-based learning (QOBL) strategies to improve the convergence rate and accuracy of the original WSO algorithm. To address the uncertainty associated with renewable energy sources, the IEEE 30 bus system, which consists of 30 buses, 6 thermal generators, and 41 branches, is modified by replacing three thermal generators with two wind generators and one solar PV generator. And the IEEE 57-bus system, which consists of 57 buses, 7 thermal generators, and 80 branches, is also modified by the same concept. The variability of wind and solar generation is described using the Weibull and lognormal distributions, and its impact on the OPF problem is considered by incorporating reserve and penalty costs for overestimation and underestimation of power output. The paper also takes into account the unpredictability of power consumption (load demand) by analyzing its influence using standard probability density functions (PDF). Furthermore, practical conditions related to the thermal generators, such as ramp rate limits are examined. The MWSO algorithm is evaluated and analyzed using 23 standard benchmark functions, and a comparative study is conducted against six well-known techniques using various statistical parameters. The results and statistical analysis demonstrate the superiority and effectiveness of the MWSO algorithm compared to the original WSO algorithm for addressing the OPF problem in the presence of generation and demand uncertainties.

The influence of training datasets on LSTM-based irradiance prediction

In solar photovoltaic power generation, the prediction of solar irradiance is essential for minimizing energy costs and ensuring the provision of high-quality electricity. Deep learning models have recently gained popularity in the field, as numerous scholars have successfully employed them to predict solar irradiance. In line with this, this paper proposes three distinct methods for dividing the training dataset. Subsequently, these methods are employed in predicting solar irradiance using the LSTM-based model. Furthermore, an error analysis of the prediction results is conducted for each of the three models. The optimal training dataset division method is determined and proposed based on a comparison of the sizes of the three models using six error evaluation indexes.

Comparison of Solar P&O and FLC-based MPPT Controllers & Analysis under Dynamic Conditions

Increase in electricity generation is caused due to population increase, which leads to the depletion of fossil fuels, and increased pollution. This leads to focusing on alternate renewable energy, mainly solar photovoltaic generation, due to the abundant availability. The maximum power generated by a PV module depends on the temperature and irradiance because the P-V and V-I natures are non-linear. Various DC-DC boost converters are used along with the MPPT techniques because the conversion efficiency of the PV system is low [1][2]. In this paper, comparative analysis between Perturb and Observe (P&O) and Fuzzy Logic-based Maximum Power Point Tracking (MPPT) systems along with modified SEPIC are done using MATLAB/ SIMULINK software. Simulations are done at different irradiations to observe its tracking speed towards MPP. From the obtained output (simulation), it is observed that the Fuzzy Logic Converter (FLC)-based MPPT controllers have good dynamic performance, reduced oscillation, high tracking speed, maximum power, etc...[3].

Analysis Of Silicon Crystal Materials: Meeting Growing Demands Across Industries

The demand for silicon crystal materials is poised for continued growth across diverse sectors in the upcoming years. In the realm of computers and communication, silicon single crystal wafers and chips are in high demand, fueled by the increasing adoption of communication equipment and the essential requirement for memory and control chips in information processing systems. Beyond the electronics domain, silicon crystals find applications in various industries, including environmental protection, new energy, and construction. In the field of environmental protection, silicon crystals play a vital role in denitrification and dust removal devices. In the new energy sector, they contribute to solar photovoltaic power generation systems and geothermal power generation systems. In construction, silicon crystals find their place in the production of building glass and semiconductor building materials. As China's economy continues to evolve and environmental concerns gain prominence, the demand for semiconductor materials, including silicon crystal materials, is anticipated to rise. This underscores a bright market outlook for silicon crystal materials in the foreseeable future.

Operation and Maintenance in Solar Plants: Eight study cases

The use of solar photovoltaic generation is growing up nowadays. New developments have been achieved lowering the prices of the modules and their installation. Nevertheless, the operation and maintenance of these plants still needs improvements in order to enhance their efficiency and economic balance. This paper presents a detailed analysis of eight photovoltaic (PV) plants revealing improvements on the monitoring system and the maintenance procedures. The common failures in PV plants have been established and their losses have been estimated. A correct diagnostic method using good information and monitoring systems could have reduced the losses.

Economic Viability of Business models for Photovoltaic solar generation in Brazil: Studies of cases

In the last years, worldwide, more capacity of solar energy has been added than any other type of electric power generation technology. The current expansion market is largely due to the increase in market competitiveness of photovoltaic solar energy and demand for electricity. However, the markets continue to be driven largely by government or regulatory incentives and not by innovative business. Therefore, this paper presents: (i) overview of photovoltaic systems regarding the most adopted business models in Brazil; ii) business model economic viability case study. The results presented shows that the acquisition model has the best investment indicators. For the Acquisition and Rental models, all case studies were economically viable, but in the Shared Solar Generation model the Consumer Units C1 and C2 were considered unfeasible by Levelized Cost of Energy - LCOE criterion.

Prediction and classification of solar photovoltaic power generation using extreme gradient boosting regression model

Abstract Solar energy is well-positioned for adoption due to the aggregate demand for renewable energy sources and the reduced price of solar panels. Solar photovoltaic (PV) electricity has many benefits over wind power, including lower noise levels, quicker installation, and more location versatility. However, there are difficulties, including the possibility of unpredictability between accessible power supply and load demand that comes with the rapid use of intermittent renewable energy sources. Hence, this study proposes the Extreme Gradient Boosting regression-based Solar Photovoltaic Power Generation Prediction (XGB-SPPGP) model to predict and classify the usage of solar power successfully with minimal error. Extreme gradient boosting regression is an effective and reliable method for solar PV power generation predictions, particularly in cases where the target-input feature relationship is complex and non-linear. Relative humidity, temperature, clear-sky index, and time of day are the most critical input features to improve the model’s accuracy. A comprehensive and reliable evaluation is provided by validating the proposed model using data from various climatic locations. The model’s performance is then further investigated by conducting a seasonal study. Solar energy has the potential to be a reliable and long-term part of the electrical power system’s growth, and these findings have significant consequences for grid management, energy planning, and governance. With generation estimation capability, an IoT-based datalogger for a stand-alone PV panel is established. The outcomes and features acquired validate the suggested methods’ superiority in forecasting electricity production. The experimental outcomes of the XGB-SPPGP model increase the power generation forecasting ratio of 99.3%, accuracy ratio of 98.7%, overall performance ratio of 97.2%, and weather prediction ratio of 95.5% and reduce mean absolute error by 8.4% compared to other popular models.

Optimal generation expansion planning model for solar PV generation on Run of River-based hydro-based power system using binary genetic algorithm

Abstract This paper presents a binary genetic algorithm approach developed to solve the optimal generation expansion planning problem for solar photo-voltaic (PV) based Run of River (ROR) hydro generation systems. The results show that the methodology effectively solves mixed-integer, constrained nonlinear generation expansion problems. The model is then used to optimize the generation expansion problem for the electricity grid in the eastern region of Nepal, considering the under construction and planned solar PV and ROR-based hydropower plants. The optimal problem is formulated to determine the optimal number, location and type of power plant to fulfill the demand for the electricity grid of the eastern zone grid of Nepal. Results show that the total generation from the optimal sites combining hydro and solar sites is found to be about 26% of that of total energy in the Nepalese grid. However, the total demand in the system under consideration for the year is about 1352 GWh. With eliminating line losses, a surplus generation of about 1377 GWh has been found. This surplus can be used as power pooling with neighboring countries. The system has a surplus during the wet season but needs to be improved during the dry season. Hence, appropriate policies can be formulated for power banking and pooling to balance the demand–supply in the system.

Using Solar PV and Stationary Storage to Buffer the Impact of Electric Minibus Charging in Grid-Constrained Sub-Saharan Africa

Despite the unstoppable global drive towards electric mobility, the electrification of sub-Saharan Africa’s ubiquitous informal multi-passenger minibus taxis raises substantial concerns. This is due to a constrained electricity system, both in terms of generation capacity and distribution networks. Without careful planning and mitigation, the additional load of charging hundreds of thousands of electric minibus taxis during peak demand times could prove catastrophic. This paper assesses the impact of charging 202 of these taxis in Johannesburg, South Africa. The potential of using external stationary battery storage and solar PV generation is assessed to reduce both peak grid demand and total energy drawn from the grid. With the addition of stationary battery storage of an equivalent of 60 kWh/taxi and a solar plant of an equivalent of 9.45 kWpk/taxi, the grid load impact is reduced by 66%, from 12 kW/taxi to 4 kW/taxi, and the daily grid energy by 58% from 87 kWh/taxi to 47 kWh/taxi. The country’s dependence on coal to generate electricity, including the solar PV supply, also reduces greenhouse gas emissions by 58%.

Accurate nowcasting of cloud cover at solar photovoltaic plants using geostationary satellite images

Accurate nowcasting for cloud fraction is still intractable challenge for stable solar photovoltaic electricity generation. By combining continuous radiance images measured by geostationary satellite and an advanced recurrent neural network, we develop a nowcasting algorithm for predicting cloud fraction at the leading time of 0–4 h at photovoltaic plants. Based on this algorithm, a cyclically updated prediction system is also established and tested at five photovoltaic plants and several stations with cloud fraction observations in China. The results demonstrate that the cloud fraction nowcasting is efficient, high quality and adaptable. Particularly, it shows an excellent forecast performance within the first 2-hour leading time, with an average correlation coefficient close to 0.8 between the predicted clear sky ratio and actual power generation at photovoltaic plants. Our findings highlight the benefits and potential of this technique to improve the competitiveness of solar photovoltaic energy in electricity market.

ANN based smart sensing of short-term voltage stability assessment of modern power system

This study addresses the critical challenge of Short-term Voltage Stability (STVS) in the contemporary electric grid system, characterized by diverse static loads, induction motors, electronic loads, electric vehicle charging stations, and renewable energy sources. High-power sensing elements are identified for power system stability and transient measurements, and the existing system is explored for fault measurement and stability maintenance. The analysis employs an IEEE 14-bus dynamic model with two distinct load configurations. In the first configuration, the static load at bus 14 is replaced with a composite load, encompassing static loads, induction motors, electronic loads, and EV charging stations. For the second novel configuration at bus 14, the static load is substituted with a composite load and a solar photovoltaic (PV) generator operating in reactive power compensation mode. This addition of the PV generator compensates for elevated reactive power demand in the 14-bus system. The study conducts voltage stability analysis in two phases, evaluating the voltage profile and transmission line load ability in both scenarios during the first phase. In the second phase, STVS is simulated by briefly opening the circuit breaker in bus 2. The results reveal notable improvements in the voltage profile of all buses, a 6 % enhancement in power factor at bus 14 in the dynamic model with the novel configuration, and a 3 % increase in the loading factor of the IEEE Bus 14 system. The dynamic model incorporating a PV generator and composite load in bus 14 demonstrates superior STVS performance compared to the setup with only a composite load. The abstract concludes by highlighting the development of real-time voltage profile assessment using Artificial Neural Network (ANN) topology, leveraging results from the Continuous Power Flow (CPF) analysis of the novel model with a PV generator and composite load in the far end bus. This restructuring ensures a logical and clear organization, encompassing an introduction to the problem, a description of methods employed, presentation of results, and a succinct conclusion summarizing the key findings.